I. Field of the Invention
This invention relates generally to biomedical monitoring apparatus, and more particularly to a non-invasive system for segregating P-wave activity from a surface electrocardiograph.
II. Background of the Invention
Automatic analysis of supraventricular arrhythmias in a surface electrocardiogram (ECG) is hampered by the difficulty of detecting P-waves reliably. Because of the relatively poor signal-to-noise ratio between P-waves and other overriding electrical activity picked up by the surface electrodes and because there is a spectral overlap between the P-waves and the QRS complex, traditional linear filtering and thresholding techniques cannot be relied upon to provide the degree of discrimination necessary to uniquely identify a ECG component as a P-wave. In a publication entitled "Automatic Tachycardia Recognition", PACE 7: Part II, 541-547, May-June 1984, Dr. Arzbaecher reported on the use of an esophageal lead including a pickup device suspended from fine wires that is swallowed so as to position the sensor in desired proximity to the heart for enhancing the detection of the P-wave apart from the QRS complex. Detection or recording apparatus is connected to leads exiting the patient's mouth. The enhanced ability to monitor supra-ventricular arrhythmias offered by this and other invasive and semi-invasive approaches emphasizes the need for a non-invasive approach for use in situations where other techniques are not clinically appropriate. It is thus the main object of the present invention to provide a non-invasive means for detecting P-waves in signals obtained from surface ECG electrodes in a reliable fashion.
In accordance with the present invention, an adaptive filter of the type described in "Adaptive Signal Processing" by B. Widrow and S. Stearns, Prentice-Hall, Inc., N.J., copyright 1985, and referred to as the Least Mean Square algorithm, is used to suppress the energy of the QRS complex in surface electrocardiograms to thereby render the P-wave more detectable in the resulting processed waveform. (Terminology such as "estimate", "error" and "desired" signals follow conventions set forth in this reference.) In the Widrow et al publication, the LMS adaptive filter technique is used as a noise canceller. Described is a scheme in which one signal, containing superimposed noise, is applied to a first channel while another signal, containing the noise alone in a form linearly related to the noise in the first channel, is applied to the second channel of the so-called "LMS filter". This filter is made to continuously adapt, ultimately converging so as to furnish an "estimate" of the noise received on the first channel. This "estimate" is subtracted from the composite signal and noise arriving on the first channel, resulting in an "error" signal which approximates the noise-free signal.
In our co-pending application filed concurrently herewith and entitled "DUAL CHANNEL COHERENT FIBRILLATION DETECTION SYSTEM", Ser. No. 025,811 the content of which is hereby incorporated by reference, there is described another application of the LMS algorithm and in that application the method of implementing the algorithm using a digital computer is set out. That application also defines various terms and parameters, again following Widrow and Stearns, which are also used herein.
In applying the LMS adaptive filter algorithm to the detection of P-waves in surface ECG waveforms, a pair of standard ECG leads positioned on the chest wall each pick up signals which respectively become the "input" and the "dresired" operands for the LMS filter. Each channel is driven by the cardiac atrial and ventricular equivalent dipoles which, in turn, comprise linear summations of cellular action potential sequences. To achieve ideal performance in suppressing the QRS component from a surface ECG lead, the surface lead should be the "desired" signal, while an independent source of the QRS signal should be used as the "input". Ideally, the "input" should derive from an endocardial ventricular catheter, since such a signal would be entirely free of the P-wave. This application differs in that both the "input" and "desired" channels contain both P-waves and QRS waves. However, by using two chest unipolar leads, such as V1 and V5, the QRS contains much more energy than the P-wave. Furthermore, the QRS morphology changes from uniphasic to biphasic to negative-uniphasic across the chest leads, while the P-wave exhibits approximately constant morphology in the same leads. By judiciously selecting the number of tap weights for the finite impulse response filter, it can be made to accommodate one but not both of the QRS complex and the P-wave. Because of the significant energy differential between the QRS complex and a P-wave, the QRS complex will have a predominant effect on the weight vector, and hence, the LMS filter will adapt primarily to the QRS complex. The phase difference which the filter must generate to cancel the QRS will also be applied to the P-wave. Subtracting the "estimate" from the "desired" now produces an "error" signal in which the QRS is effectively cancelled, while the P-wave, subjected to the same transformation, does not cancel and may even be augmented. Thus, the P/QRS energy ratio now favors P-wave detection, which may be accomplished by ordinary filtering and thresholding techniques.